This book addresses contemporary statistical inference issues when no or minimal assumptions on the nature of studied phenomenon are imposed. Information theory methods play an important role in such scenarios. The approaches discussed include various high-dimensional regression problems, time series and dependence analyses
This paper discusses how real-life statistical analysis/inference deviates from ideal environments. ...
This paper discusses how real-life statistical analysis/inference deviates from ideal environments. ...
This paper discusses how real-life statistical analysis/inference deviates from ideal environments. ...
The presented volume addresses some vital problems in contemporary statistical reasoning [...
Since the introduction of the subject of econometrics, parametric functional forms of the relationsh...
Since the introduction of the subject of econometrics, parametric functional forms of the relationsh...
This book presents new and original research in Statistical Information Theory, based on minimum div...
In summary, in the present Special Issue, manuscripts focused on any of the above-mentioned “Informa...
This book presents tools and principles of information theory as a solution to analyse insufficient i...
This book presents tools and principles of information theory as a solution to analyse insufficient i...
The combination of mathematical models and uncertainty measures can be applied in the area of data m...
AbstractEntropy and its various generalizations are widely used in mathematical statistics, communic...
The recent successes of machine learning, especially regarding systems based on deep neural networks...
This study is divided into two seemingly disjoint parts -- one containing EMPIRICAL (Bayesian and No...
This paper discusses how real-life statistical analysis/inference deviates from ideal environments. ...
This paper discusses how real-life statistical analysis/inference deviates from ideal environments. ...
This paper discusses how real-life statistical analysis/inference deviates from ideal environments. ...
This paper discusses how real-life statistical analysis/inference deviates from ideal environments. ...
The presented volume addresses some vital problems in contemporary statistical reasoning [...
Since the introduction of the subject of econometrics, parametric functional forms of the relationsh...
Since the introduction of the subject of econometrics, parametric functional forms of the relationsh...
This book presents new and original research in Statistical Information Theory, based on minimum div...
In summary, in the present Special Issue, manuscripts focused on any of the above-mentioned “Informa...
This book presents tools and principles of information theory as a solution to analyse insufficient i...
This book presents tools and principles of information theory as a solution to analyse insufficient i...
The combination of mathematical models and uncertainty measures can be applied in the area of data m...
AbstractEntropy and its various generalizations are widely used in mathematical statistics, communic...
The recent successes of machine learning, especially regarding systems based on deep neural networks...
This study is divided into two seemingly disjoint parts -- one containing EMPIRICAL (Bayesian and No...
This paper discusses how real-life statistical analysis/inference deviates from ideal environments. ...
This paper discusses how real-life statistical analysis/inference deviates from ideal environments. ...
This paper discusses how real-life statistical analysis/inference deviates from ideal environments. ...
This paper discusses how real-life statistical analysis/inference deviates from ideal environments. ...